Factors underpinning the performance of implemented artificial intelligence-based patient deterioration prediction systems: reasons for selection and implications for hospitals and researchers.

Journal: Journal of the American Medical Informatics Association : JAMIA
PMID: 39963969

Abstract

OBJECTIVE: The degree to which deployed artificial intelligence-based deterioration prediction algorithms (AI-DPA) differ in their development, the reasons for these differences, and how this may impact their performance remains unclear. Our primary objective was to identify design factors and associated decisions related to the development of AI-DPA and highlight deficits that require further research.

Authors

  • Anton H van der Vegt
    The University of Queensland, St Lucia, Qld, Australia.
  • Victoria Campbell
    Queensland Health Patient Safety Centre, Brisbane, Queensland, Australia.
  • Shuyi Wang
    Department of Gastrointestinal Surgery, Zhongnan Hospital of Wuhan University, No.169 Donghu Road, Wuhan, 430071, Hubei, China. shuyiwang@whu.edu.cn.
  • James Malycha
    University of Adelaide, Adelaide, South Australia, Australia.
  • Ian A Scott
    University of Queensland, Brisbane, Queensland, Australia (I.A.S.).

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